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Transcript
Enriching WordNet with Derivational Subnets
Karel Pala and Radek Sedláček
Faculty of Informatics, Masaryk University,
Botanicka 68a, 60200 Brno, Czech Republic
{pala, [email protected]
Abstract. In this paper, we deal with the derivational (word formation)
relations as they are handled by the Czech morphological module Ajka. First,
we show that they represent empirically well-based semantic relations forming
small semantic networks, and then we solve the problem how to integrate them
into lexical database such as (Czech) WordNet. In this respect we examine the
relation between the derivational relations and semantic roles (deep cases)
defined as Internal Language Relations in EuroWordNet. An attempt is made to
match up the inventory of the semantic roles in EWN with the derivational
(semantic) relations. We also use a tool called SAFT that can process a raw
(corpus) text in such a way that it uses module Ajka to find links relating the
WordNet senses to the noun and verbal lemmata obtained from the raw (corpus)
text. This technique allows us to enrich Czech WordNet with the derivational
subnets and represent them in a XML format. The result is a new kind of the
semantic network, which consists of two layers, upper and lower. The result is a
more powerful and efficient resource for applications like tools for WSD, web
searching or information extraction.
1. Derivational Relations as Semantic Networks
For computer processing highly inflected language like Czech it is necessary to have a
high quality morphological module that can perform lemmatization of a given word
form and yield all the grammatical categories that are carried by the word form. Such
a tool for Czech is a morphological analyzer and generator called Ajka developed in
NLP Lab at FI MU (Sedláček, 2001, 2004). Other tools exist for Czech as well (Hajič,
2004) but we prefer Ajka for its properties – it is able to deal with derivational
relations automatically.
Ajka is based on the system of the (approx.) 2000 inflectional paradigms, contains
about 350 000 Czech stems and is able to generate about 5,7 million Czech word
forms. Its coverage/recall for Czech is about 96 % (tested on the corpus All
containing 640 mil. Czech word forms and implemented in the NLP Lab at FI MU). It
is based on the ‘paradigmatic’ model of morphology and though it has been primarily
devised for Czech its engine can work also with other synthetic languages (such as
Slavonic, e.g. Slovak, Serbian, Russian) as well as with analytic ones – there are
versions for English, German, French, Dutch, Spanish, Italian.
As we said the morphological module Ajka captures not only the inflectional
relations but also the derivation ones (word formation relations). For Czech we know
2
Karel Pala and Radek Sedláček
that approximately 67 % of the word stock is obtained by means of the word
formation and that the derivation of the new words is highly regular and can be
described by the formal rules. The word formation rules have been recently integrated
into Ajka (Sedláček, 2004) so that it is now able to generate and recognize word
derivational networks (subnets) automatically. An example of such derivational nest
is given in Fig. 1 (both for English and Czech, actual output from Ajka looks slightly
different):
DIM
book (kniha)
ADJ
booklet (knížka)
PROP
bookish (knižní)
bookishness (knižnost)
ADV
LOC
bookishly (knižně)
bookcase (knihovna)
ACT
bookbinder (knihař)
Figure 1 Word derivation graph (subnet) for the root book
One can observe that the semantic relations between the above items are
empirically well founded since they can be deduced from the given root or stem and
the respective suffixes indicated in bold face (or prefixes as well). They represent a
part of the language knowledge that speakers have. The derivational semantic
network can be formally represented as a graph with one or more roots (see Figure 1).
Its nodes represent the individual lemmata or word forms and edges can be labelled
by the corresponding semantic relations, which follow from the relation between roots
and derivational suffixes. The following example with home shows that derivational
relations in English are reasonably rich, and in fact, follow almost the same principles
as in Czech which, however, is more regular and productive (the data comes from
BNC).
As a NOUN
In Real Estate As an ADJ
domain:
As an ADV
As a VERB
home
homework
homeland
homecoming
homeboy
homestead
homecare
homebase
homebrew
(the) homeless
homelessness
homeliness
homeowner
homebuyer
homeloan
homecover
home
(e.g. go home,
I am at home)
homeward
homewards
home
(e.g. If you
want to home
to a beacon…)
phrasal verb:
to home in on
homesick
home-made
home-based
homegrown
homeless
homely
Enriching WordNet with Derivational Subnets
3
2. Are Derivational Relations Semantic?
In Czech linguistic works related to the word formation (Dokulil, 1962) the
derivational relations are treated as a special group of the relations that express
“semantic relations sui generis”, i.e. they are understood as different from other
„standard“ semantic relations based on the sentence constituents. In our opinion, this
differentiation can be empirically justified since the derivational relations are in fact
morphological relations whereas “standard” semantic roles are viewed as the relations
resting on sentence constituents.
However, if we have a look at the collection of the Czech derivational suffixes (67 in
Ajka), we can distinguish various types of the derivational relations expressing the
particular semantic relations as e.g. agentive (to teach -.teacher) or expressing
property (home – homelessness). According to our intuition they can be seen as
similar to other semantic relations usually characterized as “semantic roles” or
“semantic cases” but they are obtained in a different way, i.e. derivationally
(morphologically). While the semantic roles are typically associated with verbs and
their arguments, the derivational relations hold between the four open parts of speech
(in many languages), i.e. we have derivational pairs like noun – adjective or adjective
– noun, noun – verb or verb – noun, noun – noun, adjective – adverb. Thus, formally
they go across the individual parts of speech being XPOS relations. Though it
requires more complete examination of the derivational data, the intuition is that
basically there should not be an essential difference between “derivational” semantic
relations and “sentence” semantic relations resting on predicate-argument structure of
verbs. From the cognitive point of view the Occams Razor principle supports this
intuition as well.
Below we are proposing a labelling (tagging) that can be used for the individual
derivational relations and capture their semantic nature. It is tentative and it should be
further refined when the empirical data becomes more complete. The labels in the list
below are experimentally sub-classified (by numbers showing more detailed semantic
differences) but it is not the only possible solution.
The tentative list of the derivational relations below is based on the rich Czech data
but if the corresponding semantic relations can be considered rather universal (we
think so), then we are convinced that they can be applied also in other languages such
as English or German (not speaking about Slavonic ones).





AG0, agent performing an action: teacher (učitel) from to teach (učit),
AG1, agent producing an object: glassmaker (sklář) from glass (sklo),
PROPB0, ownership of an object: farmer (statkář) from farm (statek),
PROPB1, pertaining to an object: villager (vesničan from village (vesnice),
INS, means or instrument by which an action is performed: excavator (rypadlo)
from to excavate (rýpat),
4
Karel Pala and Radek Sedláček
 PAT, patient of an action: prisoner (vězeň) from imprison (uvěznit),
 RES, result of an action: printed copy (výtisk) from to print (tisknout),
 PROP (XPOS), property expressed by noun: quickness (rychlost) from adjective
quick (rychlý),
 PROP1, property, diligent (pilný) from diligence (píle),
 ACT (XPOS), action verb – noun: the fall, falling (pád, padání) from to fall
(padat),
 ACTPROP, property changing to an action: become green (zelenat) from green
(zelený),
 PROPMANN (XPOS), property of the action, i..e. manner: quickly (rychle) from
quick (rychlý),
 PROPDIM, diminutive relation: booklet (knížečka) from book (kniha),
 PROPAUG, augmentative: big oak (dubisko) from oak (dub),
 PROPGEN, shift of gender: female teacher (učitelka) from teacher (učitel),
PROPYOUNG, young animal: lion cub (lvíče) from lion (lev),
 POSS, possessive, father’s (otcův) from father (otec),
 LOC, location: battlefield (bojiště from battle (boj).
3. Adding Semantic (derivational) Subnets into WordNet
As it follows from the above, we list above 17 derivational (semantic) relations that
are morphologically well justified by the respective suffixes or morphemes (in
English). The complete list will be a bit larger and the labelling is still tentative but
the main point is that the indicated relations have a firm empirical (and formal) base.
To prove the basis of the abovementioned intuition concerning the unity of the
semantic relations we find it is useful to compare them with an inventory of semantic
roles, particularly with the semantic roles that have been defined within the set of the
Internal Language Relations introduced in EuroWordNet (Vossen, 1999). We find the
following 15 roles there:














ROLE_AGENT – INVOLVED_AGENT
ROLE_PATIENT – INVOLVED_PATIENT
ROLE_INSTRUMENT – INVOLVED_INSTRUMENT
ROLE_LOCATION – INVOLVED_LOCATION
ROLE_SOURCE_DIRECTION
ROLE_TARGET_DIRECTION
STATE_OF – BE_IN_STATE
CAUSES – IS_CAUSED_BY
HAS_SUBEVENT – IS_SUBEVENT_OF
XPOS_NEAR_SYNONYM
XPOS_NEAR_ANTONYM
ROLE_RESULT – INVOLVED_RESULT
IS_MANNER_FOR – IN_MANNER
DERIVES – DERIVED FROM
Enriching WordNet with Derivational Subnets

5
DERIVATIVE (defined in PWN v.2, not in EWN)
Obviously, roles like AGENT, PAT, INSTR, RES, LOC, MANN can be found in
both lists but still, there is a question how similar they are. We have to be aware of
the fact that ILRs are not always associated with the synsets while the derivational
relations are always associated with the literals representing the individual items
within the synsets (being XPOS relations). In this way with derivational relations we
obtain denser network containing not more relations but between more lexical items.
Some ILRs, e.g. DIRECTION, CAUSES, SUBEVENT, do not occur according to
our knowledge (at least in Czech) as derivational so they can be kept and used in the
same way as in EWN. The role SUBEVENT can be exploited to capture the aspect
relations like Perfective – Imperfective – Iterative, which in Czech and other Slavonic
languages are not treated as derivational but morphological, aspect is an obligatory
grammatical category that has to be expressed by each Czech (Slavonic) verb. E.g.
přečíst (to read to the end, read through) can be considered as a subevent of číst
(read), but the category of the aspect is not so broad in Czech, so this is rather a
tentative solution. In Czech WordNet we record aspect pairs (Perfective –
Imperfective) associated with the individual verbs. The iterative verbs are obtained
directly from Ajka through the respective derivational relation.
The special case is the relation DERIVED which was introduced into EWN to capture
derivational relations existing in some EWN languages, however, according to our
knowledge it was not elaborated in the way we do it here.
In fact, the role DERIVED was designed to cover any derivational relation that can
occur between two synsets or literals, and in this respect, it is too general. However, it
should be noted that in Princeton WordNet v.2 (PWN2) there is a relation
DERIVATIVE which covers derivational relations between nouns and verbs (teach –
teacher), adjectives and adverbs (quick – quickly) but not relations between nouns and
adjectives like stupidity – stupid). Thanks to multilingual WordNets as in
EuroWordNet or Balkanet the relation DERIVATIVE as it exists in PWN2 can be
translated into other languages that are linked to English via Interlingual Index (ILI).
But obviously, it can also work the other way around, i.e. if e.g. rich Czech
derivational relations (together with their semantic labels) are integrated into Czech
WordNet it is possible to exploit ILIs in another direction, i.e. from Czech to English
and “derivational” semantic relation can be reflected in English as well.
Originally, the ILRs have been employed in the process of connecting hyperonyms
with their respective hyponyms and holonyms with their meronyms, however the
XML representation of the ILRs fundamentally allows us to capture any type of
general relations.
The important result is: as we have indicated above – thanks to module Ajka we are
able to work with the derivational relations automatically. Therefore, we can
introduce them into Czech WordNet and exploit them in various ways there
automatically as well. The derivational relations also can help considerably in a more
reliable discrimination of the individual senses, which are sometimes too fine-grained
(especially in PWN2).
6
Karel Pala and Radek Sedláček
4. Morphological Interface for Czech WordNet - Saft
WordNet synset literals are naturally stored as lemmata. That is why we cannot
use plain text as an input stream for any kind of analysis. It is obvious that if we have
large amount of data to be semantically tagged, we cannot use WordNet as it stands
(at least in Czech).
The Ajka module mentioned above can be fully exploited as a bottom module for
other applications. We want to exploit its ability to find a lemma for each word form
in a text and associate it with its derivational subnet as we demonstrated above. The
only problem is that Ajka as such is limited in one relevant respect: it can only
process the words one by one as separate units.
For this purpose we have implemented a tool that employs Ajka and handles the
multi-word expressions (collocations). It is named Mwe (Svoboda 2003) and uses
Ajka as its bottom module. It recognizes multi-word expressions (MWE) that occur in
Czech WordNet and many others. They are: collocations (cumulative shot, diamond
dust, dipterous insect), proper, geographical and other names (Albert Einstein,
Lisabon, Kuril Islands, Matrix Reloaded) and abbreviations (NATO, colloq., A.D.).
Each recognized collocation is associated with its unique lemma. We can easily see
from corpus texts or magazine articles that there is approximately one MWE in every
other sentence.
In WordNet (both English and Czech) we find about 40 % collocations so it is
obvious that if we want to semantically tag a sentence where the collocation
'cumulative shot' occurs, we must recognize it as a whole. If simple analysis uncovers
that 'cumulative' is a lemma and 'shot' is a lemma, and then we would manually look
up for these two words, we will get plenty of false hits. It is likely that the desired
synset will be among them but still the other synsets are unwanted when we process
the data automatically. Collocations (or their lemmata) tend to display only one
semantic unit, so if we recognize them as a whole, we practically recognize them
unambiguously.
The implementation of the idea discussed above, i.e. interconnection of the
functionalities of the Ajka and MWE tool with Czech WordNet can be found in the
module called Saft (Čapek, 2004). It takes plain text as its input, parses it and
recognizes the collocations, then looks them up in WordNet. Single-word expressions
are processed by Ajka directly. Saft is now able to analyze and lemmatize any text in
Czech and to associate relevant lemmata with the appropriate literals in Czech
WordNet.
It should be noted that no attempt is made to disambiguate senses that may be
associated with the individual literals. It is also possible to generate identification
numbers of the synsets containing these literals and import them into VisDic (Smrž,
Horák, 2004), which is the tool for storing, managing and editing WordNet lexical
databases.
Conclusions
In the presented paper we offer the description of the selected derivational relations in
Czech and their implementation in morphological analyzer Ajka, which is able to
Enriching WordNet with Derivational Subnets
7
generate the derivational semantic networks). Then we show what semantic relations
they capture and compare them briefly with the ILRs as they are defined in
EuroWordNet. The comparison leads us to the conclusion that they are in many
respects similar if not the same: ILRs are implicitly associated with the sentence
constituents whereas the derivational relations (DR) rest on the morphological
relations.
The derivational relations are labelled semantically and they are presented in the list
containing 17 semantic relations. The list of ILRs from EuroWordNet contains 15
relations.
Then we show how the DR can be integrated into Czech WordNet. A tool called
Saft is mentioned that makes it possible to process a free (corpus) text and search both
for the individual synsets and literals linked with DRs. This does not mean that we do
a semantic disambiguation, the described processing is only a necessary first step that
has to be done in any case.
As a result we obtain a Czech WordNet in which we have two levels of the semantic
relations – the first one are ILRs and the second one are DRs. They are more subtle
and detailed than ILRs, thus they yield more powerful resource for Information
Extraction and Web searching. In this sense DRs represent a subnet that relates the
individual literals above the standard synonymy/antonymy and hypero/hyponymy
relations.
References
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